IQMs for functional images¶
Measures for the structural information¶
Definitions are given in the summary of structural IQMs.
- Entropy-focus criterion (
efc()
). - Foreground-Background energy ratio (
fber()
, [Shehzad2015]). - Full-width half maximum smoothness (
fwhm_*
). - Signal-to-noise ratio (
snr()
). - Summary statistics (
summary_*_*
).
Measures for the temporal information¶
- DVARS - D referring to temporal derivative of timecourses, VARS referring to
RMS variance over voxels (
dvars
), calculated with nipype before motion correction. - Ghost to Signal Ratio (
gsr()
,ghost_*
: along the two possible phase-encoding axes x, y. - Global Correlation (
gcor()
,gcor
). - Temporal SNR (tSNR,
tsnr
) is the median value of the tSNR map.
Measures for artifacts and other¶
- Framewise Displacement (
mean_fd
, [Power2012]). - Number of timepoints above FD theshold (
num_fd
): the threshold is defined at 0.20mm, so FD \(> 0.20mm\) - Percent of ``num_fd`` w.r.t. the timeseries.
- Outlier fraction (
outlier
) - Mean fraction of outliers per fMRI volume as given by AFNI. - Quality index (
quality
) - Mean quality index as computed by AFNI.
References
[Atkinson1997] | Atkinson et al., Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion, IEEE Trans Med Imag 16(6):903-910, 1997. doi:10.1109/42.650886. |
[Friedman2008] | Friedman, L et al., Test–retest and between‐site reliability in a multicenter fMRI study. Hum Brain Mapp, 29(8):958–972, 2008. doi:10.1002/hbm.20440. |
[Giannelli2010] | Giannelli et al., Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing. J App Clin Med Phy, 11(4). 2010. doi:10.1120/jacmp.v11i4.3237. |
[Jenkinson2002] | Jenkinson et al., Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841, 2002. doi:10.1006/nimg.2002.1132. |
[Nichols2013] | Nichols, Notes on Creating a Standardized Version of DVARS, 2013. |
[Power2012] | Power et al., Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, NeuroImage 59(3):2142-2154, 2012, doi:10.1016/j.neuroimage.2011.10.018. |
[Saad2013] | Saad et al. Correcting Brain-Wide Correlation Differences in Resting-State FMRI, Brain Conn 3(4):339-352, 2013, doi:10.1089/brain.2013.0156. |
mriqc.qc.functional module¶
-
mriqc.qc.functional.
gcor
(func, mask=None)[source]¶ Compute the GCOR [Saad2013].
Parameters: - func (numpy.ndarray) – input fMRI dataset, after motion correction
- mask (numpy.ndarray) – 3D brain mask
Returns: the computed GCOR value
-
mriqc.qc.functional.
gsr
(epi_data, mask, direction=u'y', ref_file=None, out_file=None)[source]¶ Computes the GSR [Giannelli2010]. The procedure is as follows:
- Create a Nyquist ghost mask by circle-shifting the original mask by \(N/2\).
- Rotate by \(N/2\)
- Remove the intersection with the original mask
- Generate a non-ghost background
- Calculate the GSR
Warning
This should be used with EPI images for which the phase encoding direction is known.
Parameters: Returns: the computed gsr